Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By : Joseph Howse, Joe Minichino
Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.
Table of Contents (13 chapters)

Finding trends in motion using the Kalman filter

The Kalman filter is an algorithm developed mainly (but not exclusively) by Rudolf Kalman in the late 1950s. It has found practical applications in many fields, particularly navigation systems for all sorts of vehicles from nuclear submarines to aircraft.

The Kalman filter operates recursively on a stream of noisy input data to produce a statistically optimal estimate of the underlying system state. In the context of computer vision, the Kalman filter can smoothen the estimate of a tracked object's position.

Let's consider a simple example. Think of a small red ball on a table and imagine you have a camera pointing at the scene. You identify the ball as the subject to be tracked, and flick it with your fingers. The ball will start rolling on the table in accordance with the laws of motion.

If the ball is rolling at a...